Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network - Muhammad Sidik, Siti Syatirah (2023)
One of the alternatives to improve the modeling of the Discrete Hopfield Neural Network is by implementing different variants of logical rules. In this context, Satisfiability is suitable as a logical rule in Discrete Hopfield Neural Network due to the simplicity of the structure, and fault tolerance. Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. The Binary Artificial Bee Colony will be utilized to optimize the logical structure according to the ratio of negative literals by capitalizing the features of the exploration mechanism of the algorithm. Then, the Election algorithm will be utilized to obtain a satisfied interpretation of the correct logical structure in the training phase of the Discrete Hopfield Neural Network. This proposed model will be employed in the Improved Reverse Analysis method to extract the relationship between various fields of real-life data sets based on logical representation. This thesis will be presented by implementing simulated, and benchmark data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperforms other models.